| import gc |
| import tempfile |
|
|
| import torch |
|
|
| from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline |
| from diffusers.loaders.single_file_utils import _extract_repo_id_and_weights_name |
| from diffusers.utils import load_image |
|
|
| from ..testing_utils import ( |
| backend_empty_cache, |
| enable_full_determinism, |
| numpy_cosine_similarity_distance, |
| require_torch_accelerator, |
| slow, |
| torch_device, |
| ) |
| from .single_file_testing_utils import ( |
| SDXLSingleFileTesterMixin, |
| download_diffusers_config, |
| download_single_file_checkpoint, |
| ) |
|
|
|
|
| enable_full_determinism() |
|
|
|
|
| @slow |
| @require_torch_accelerator |
| class TestStableDiffusionXLControlNetPipelineSingleFileSlow(SDXLSingleFileTesterMixin): |
| pipeline_class = StableDiffusionXLControlNetPipeline |
| ckpt_path = "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors" |
| repo_id = "stabilityai/stable-diffusion-xl-base-1.0" |
| original_config = ( |
| "https://raw.githubusercontent.com/Stability-AI/generative-models/main/configs/inference/sd_xl_base.yaml" |
| ) |
|
|
| def setup_method(self): |
| gc.collect() |
| backend_empty_cache(torch_device) |
|
|
| def teardown_method(self): |
| gc.collect() |
| backend_empty_cache(torch_device) |
|
|
| def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0): |
| generator = torch.Generator(device=generator_device).manual_seed(seed) |
| image = load_image( |
| "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png" |
| ) |
| inputs = { |
| "prompt": "Stormtrooper's lecture", |
| "image": image, |
| "generator": generator, |
| "num_inference_steps": 2, |
| "strength": 0.75, |
| "guidance_scale": 7.5, |
| "output_type": "np", |
| } |
|
|
| return inputs |
|
|
| def test_single_file_format_inference_is_same_as_pretrained(self): |
| controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16) |
| pipe_single_file = self.pipeline_class.from_single_file( |
| self.ckpt_path, controlnet=controlnet, torch_dtype=torch.float16 |
| ) |
| pipe_single_file.unet.set_default_attn_processor() |
| pipe_single_file.enable_model_cpu_offload(device=torch_device) |
| pipe_single_file.set_progress_bar_config(disable=None) |
|
|
| inputs = self.get_inputs(torch_device) |
| single_file_images = pipe_single_file(**inputs).images[0] |
|
|
| pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet, torch_dtype=torch.float16) |
| pipe.unet.set_default_attn_processor() |
| pipe.enable_model_cpu_offload(device=torch_device) |
|
|
| inputs = self.get_inputs(torch_device) |
| images = pipe(**inputs).images[0] |
|
|
| assert images.shape == (512, 512, 3) |
| assert single_file_images.shape == (512, 512, 3) |
|
|
| max_diff = numpy_cosine_similarity_distance(images[0].flatten(), single_file_images[0].flatten()) |
| assert max_diff < 5e-2 |
|
|
| def test_single_file_components(self): |
| controlnet = ControlNetModel.from_pretrained( |
| "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
| ) |
| pipe = self.pipeline_class.from_pretrained( |
| self.repo_id, |
| variant="fp16", |
| controlnet=controlnet, |
| torch_dtype=torch.float16, |
| ) |
|
|
| pipe_single_file = self.pipeline_class.from_single_file(self.ckpt_path, controlnet=controlnet) |
| super().test_single_file_components(pipe, pipe_single_file) |
|
|
| def test_single_file_components_local_files_only(self): |
| controlnet = ControlNetModel.from_pretrained( |
| "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
| ) |
| pipe = self.pipeline_class.from_pretrained( |
| self.repo_id, |
| variant="fp16", |
| controlnet=controlnet, |
| torch_dtype=torch.float16, |
| ) |
|
|
| with tempfile.TemporaryDirectory() as tmpdir: |
| repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) |
| local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) |
|
|
| single_file_pipe = self.pipeline_class.from_single_file( |
| local_ckpt_path, controlnet=controlnet, safety_checker=None, local_files_only=True |
| ) |
|
|
| self._compare_component_configs(pipe, single_file_pipe) |
|
|
| def test_single_file_components_with_original_config(self): |
| controlnet = ControlNetModel.from_pretrained( |
| "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
| ) |
| pipe = self.pipeline_class.from_pretrained( |
| self.repo_id, |
| variant="fp16", |
| controlnet=controlnet, |
| torch_dtype=torch.float16, |
| ) |
|
|
| pipe_single_file = self.pipeline_class.from_single_file( |
| self.ckpt_path, |
| original_config=self.original_config, |
| controlnet=controlnet, |
| ) |
| self._compare_component_configs(pipe, pipe_single_file) |
|
|
| def test_single_file_components_with_original_config_local_files_only(self): |
| controlnet = ControlNetModel.from_pretrained( |
| "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
| ) |
| pipe = self.pipeline_class.from_pretrained( |
| self.repo_id, |
| variant="fp16", |
| controlnet=controlnet, |
| torch_dtype=torch.float16, |
| ) |
|
|
| with tempfile.TemporaryDirectory() as tmpdir: |
| repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) |
| local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) |
|
|
| pipe_single_file = self.pipeline_class.from_single_file( |
| local_ckpt_path, |
| safety_checker=None, |
| controlnet=controlnet, |
| local_files_only=True, |
| ) |
| self._compare_component_configs(pipe, pipe_single_file) |
|
|
| def test_single_file_components_with_diffusers_config(self): |
| controlnet = ControlNetModel.from_pretrained( |
| "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
| ) |
| pipe = self.pipeline_class.from_pretrained(self.repo_id, controlnet=controlnet) |
| pipe_single_file = self.pipeline_class.from_single_file( |
| self.ckpt_path, controlnet=controlnet, config=self.repo_id |
| ) |
|
|
| super()._compare_component_configs(pipe, pipe_single_file) |
|
|
| def test_single_file_components_with_diffusers_config_local_files_only(self): |
| controlnet = ControlNetModel.from_pretrained( |
| "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
| ) |
| pipe = self.pipeline_class.from_pretrained( |
| self.repo_id, |
| controlnet=controlnet, |
| ) |
|
|
| with tempfile.TemporaryDirectory() as tmpdir: |
| repo_id, weight_name = _extract_repo_id_and_weights_name(self.ckpt_path) |
| local_ckpt_path = download_single_file_checkpoint(repo_id, weight_name, tmpdir) |
| local_diffusers_config = download_diffusers_config(self.repo_id, tmpdir) |
|
|
| pipe_single_file = self.pipeline_class.from_single_file( |
| local_ckpt_path, |
| config=local_diffusers_config, |
| safety_checker=None, |
| controlnet=controlnet, |
| local_files_only=True, |
| ) |
| super()._compare_component_configs(pipe, pipe_single_file) |
|
|
| def test_single_file_setting_pipeline_dtype_to_fp16(self): |
| controlnet = ControlNetModel.from_pretrained( |
| "diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" |
| ) |
| single_file_pipe = self.pipeline_class.from_single_file( |
| self.ckpt_path, controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 |
| ) |
| super().test_single_file_setting_pipeline_dtype_to_fp16(single_file_pipe) |
|
|
